Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations511457
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory70.2 MiB
Average record size in memory144.0 B

Variable types

Numeric11
Categorical6

Alerts

ON_NET has constant value "27.0" Constant
ORANGE has constant value "29.0" Constant
TIGO has constant value "6.0" Constant
MRG has constant value "0" Constant
FREQ_TOP_PACK has constant value "5.0" Constant
ARPU_SEGMENT is highly overall correlated with FREQUENCE and 1 other fieldsHigh correlation
FREQUENCE is highly overall correlated with ARPU_SEGMENT and 1 other fieldsHigh correlation
FREQUENCE_RECH is highly overall correlated with MONTANTHigh correlation
MONTANT is highly overall correlated with FREQUENCE_RECHHigh correlation
REVENUE is highly overall correlated with ARPU_SEGMENT and 1 other fieldsHigh correlation
TOP_PACK is highly skewed (γ1 = 73.61180278) Skewed
user_id has unique values Unique
REGION has 464072 (90.7%) zeros Zeros

Reproduction

Analysis started2025-07-28 11:56:30.791118
Analysis finished2025-07-28 11:57:03.655953
Duration32.86 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

user_id
Real number (ℝ)

Unique 

Distinct511457
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1075925
Minimum1
Maximum2154045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 MiB
2025-07-28T14:57:03.739987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile107304.8
Q1538252
median1074663
Q31613435
95-th percentile2045543.2
Maximum2154045
Range2154044
Interquartile range (IQR)1075183

Descriptive statistics

Standard deviation621569.56
Coefficient of variation (CV)0.57770713
Kurtosis-1.198197
Mean1075925
Median Absolute Deviation (MAD)537594
Skewness0.0015387196
Sum5.5028939 × 1011
Variance3.8634871 × 1011
MonotonicityStrictly increasing
2025-07-28T14:57:03.868358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2154045 1
 
< 0.1%
2154002 1
 
< 0.1%
2153996 1
 
< 0.1%
2153994 1
 
< 0.1%
2153990 1
 
< 0.1%
2153987 1
 
< 0.1%
2153982 1
 
< 0.1%
2153980 1
 
< 0.1%
2153974 1
 
< 0.1%
2153970 1
 
< 0.1%
Other values (511447) 511447
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
6 1
< 0.1%
10 1
< 0.1%
12 1
< 0.1%
17 1
< 0.1%
20 1
< 0.1%
22 1
< 0.1%
35 1
< 0.1%
46 1
< 0.1%
51 1
< 0.1%
ValueCountFrequency (%)
2154045 1
< 0.1%
2154043 1
< 0.1%
2154041 1
< 0.1%
2154034 1
< 0.1%
2154033 1
< 0.1%
2154032 1
< 0.1%
2154029 1
< 0.1%
2154028 1
< 0.1%
2154027 1
< 0.1%
2154024 1
< 0.1%

REGION
Real number (ℝ)

Zeros 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.68073758
Minimum0
Maximum13
Zeros464072
Zeros (%)90.7%
Negative0
Negative (%)0.0%
Memory size7.8 MiB
2025-07-28T14:57:03.951524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.4493177
Coefficient of variation (CV)3.5980351
Kurtosis13.154023
Mean0.68073758
Median Absolute Deviation (MAD)0
Skewness3.7606817
Sum348168
Variance5.9991572
MonotonicityNot monotonic
2025-07-28T14:57:04.047347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 464072
90.7%
12 10593
 
2.1%
4 6759
 
1.3%
9 6105
 
1.2%
1 5597
 
1.1%
7 5407
 
1.1%
11 2730
 
0.5%
3 2055
 
0.4%
6 1971
 
0.4%
2 1934
 
0.4%
Other values (4) 4234
 
0.8%
ValueCountFrequency (%)
0 464072
90.7%
1 5597
 
1.1%
2 1934
 
0.4%
3 2055
 
0.4%
4 6759
 
1.3%
5 121
 
< 0.1%
6 1971
 
0.4%
7 5407
 
1.1%
8 1887
 
0.4%
9 6105
 
1.2%
ValueCountFrequency (%)
13 1925
 
0.4%
12 10593
2.1%
11 2730
 
0.5%
10 301
 
0.1%
9 6105
1.2%
8 1887
 
0.4%
7 5407
1.1%
6 1971
 
0.4%
5 121
 
< 0.1%
4 6759
1.3%

TENURE
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8109538
Minimum0
Maximum7
Zeros65
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.8 MiB
2025-07-28T14:57:04.123999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q17
median7
Q37
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.75022196
Coefficient of variation (CV)0.11014932
Kurtosis19.616014
Mean6.8109538
Median Absolute Deviation (MAD)0
Skewness-4.3517824
Sum3483510
Variance0.562833
MonotonicityNot monotonic
2025-07-28T14:57:04.186867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
7 475499
93.0%
5 15063
 
2.9%
4 8671
 
1.7%
3 5456
 
1.1%
6 3874
 
0.8%
2 2577
 
0.5%
1 252
 
< 0.1%
0 65
 
< 0.1%
ValueCountFrequency (%)
0 65
 
< 0.1%
1 252
 
< 0.1%
2 2577
 
0.5%
3 5456
 
1.1%
4 8671
 
1.7%
5 15063
 
2.9%
6 3874
 
0.8%
7 475499
93.0%
ValueCountFrequency (%)
7 475499
93.0%
6 3874
 
0.8%
5 15063
 
2.9%
4 8671
 
1.7%
3 5456
 
1.1%
2 2577
 
0.5%
1 252
 
< 0.1%
0 65
 
< 0.1%

MONTANT
Real number (ℝ)

High correlation 

Distinct113
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2892.0844
Minimum20
Maximum8100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 MiB
2025-07-28T14:57:04.293387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile3000
Q13000
median3000
Q33000
95-th percentile3000
Maximum8100
Range8080
Interquartile range (IQR)0

Descriptive statistics

Standard deviation530.88017
Coefficient of variation (CV)0.18356316
Kurtosis20.092572
Mean2892.0844
Median Absolute Deviation (MAD)0
Skewness-4.5812686
Sum1.4791768 × 109
Variance281833.76
MonotonicityNot monotonic
2025-07-28T14:57:04.422923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3000 490071
95.8%
200 5912
 
1.2%
500 5801
 
1.1%
100 4057
 
0.8%
250 1284
 
0.3%
1000 1209
 
0.2%
300 913
 
0.2%
400 327
 
0.1%
150 315
 
0.1%
600 306
 
0.1%
Other values (103) 1262
 
0.2%
ValueCountFrequency (%)
20 1
 
< 0.1%
30 2
 
< 0.1%
50 108
 
< 0.1%
100 4057
0.8%
125 1
 
< 0.1%
130 1
 
< 0.1%
150 315
 
0.1%
195 1
 
< 0.1%
200 5912
1.2%
210 1
 
< 0.1%
ValueCountFrequency (%)
8100 1
 
< 0.1%
8000 1
 
< 0.1%
7750 1
 
< 0.1%
7700 1
 
< 0.1%
7500 3
< 0.1%
7200 1
 
< 0.1%
7000 2
< 0.1%
6850 2
< 0.1%
6600 1
 
< 0.1%
6000 3
< 0.1%

FREQUENCE_RECH
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7524269
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 MiB
2025-07-28T14:57:04.529400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q17
median7
Q37
95-th percentile7
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1867503
Coefficient of variation (CV)0.17575168
Kurtosis19.186098
Mean6.7524269
Median Absolute Deviation (MAD)0
Skewness-4.5977819
Sum3453576
Variance1.4083764
MonotonicityNot monotonic
2025-07-28T14:57:04.609137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
7 490023
95.8%
1 19982
 
3.9%
2 1150
 
0.2%
3 176
 
< 0.1%
4 63
 
< 0.1%
5 38
 
< 0.1%
6 20
 
< 0.1%
9 3
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
1 19982
 
3.9%
2 1150
 
0.2%
3 176
 
< 0.1%
4 63
 
< 0.1%
5 38
 
< 0.1%
6 20
 
< 0.1%
7 490023
95.8%
8 2
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
9 3
 
< 0.1%
8 2
 
< 0.1%
7 490023
95.8%
6 20
 
< 0.1%
5 38
 
< 0.1%
4 63
 
< 0.1%
3 176
 
< 0.1%
2 1150
 
0.2%
1 19982
 
3.9%

REVENUE
Real number (ℝ)

High correlation 

Distinct842
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2866.2131
Minimum1
Maximum5680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 MiB
2025-07-28T14:57:04.705993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3000
Q13000
median3000
Q33000
95-th percentile3000
Maximum5680
Range5679
Interquartile range (IQR)0

Descriptive statistics

Standard deviation611.69683
Coefficient of variation (CV)0.21341638
Kurtosis17.030692
Mean2866.2131
Median Absolute Deviation (MAD)0
Skewness-4.3474669
Sum1.4659448 × 109
Variance374173.01
MonotonicityNot monotonic
2025-07-28T14:57:04.839118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3000 487778
95.4%
1 1989
 
0.4%
20 1434
 
0.3%
2 1363
 
0.3%
4 1105
 
0.2%
10 1096
 
0.2%
100 885
 
0.2%
11 755
 
0.1%
200 740
 
0.1%
8 706
 
0.1%
Other values (832) 13606
 
2.7%
ValueCountFrequency (%)
1 1989
0.4%
2 1363
0.3%
3 27
 
< 0.1%
4 1105
0.2%
5 27
 
< 0.1%
6 611
 
0.1%
7 338
 
0.1%
8 706
 
0.1%
9 664
 
0.1%
10 1096
0.2%
ValueCountFrequency (%)
5680 1
 
< 0.1%
5649 1
 
< 0.1%
5502 1
 
< 0.1%
5498 1
 
< 0.1%
5410 1
 
< 0.1%
5399 1
 
< 0.1%
5350 6
< 0.1%
5338 1
 
< 0.1%
5300 9
< 0.1%
5279 1
 
< 0.1%

ARPU_SEGMENT
Real number (ℝ)

High correlation 

Distinct463
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean955.40397
Minimum0
Maximum1893
Zeros1989
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size7.8 MiB
2025-07-28T14:57:04.972560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1000
Q11000
median1000
Q31000
95-th percentile1000
Maximum1893
Range1893
Interquartile range (IQR)0

Descriptive statistics

Standard deviation203.90096
Coefficient of variation (CV)0.21341858
Kurtosis17.030819
Mean955.40397
Median Absolute Deviation (MAD)0
Skewness-4.3474771
Sum4.8864805 × 108
Variance41575.602
MonotonicityNot monotonic
2025-07-28T14:57:05.107933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 487781
95.4%
1 2495
 
0.5%
3 2466
 
0.5%
0 1989
 
0.4%
7 1830
 
0.4%
4 1593
 
0.3%
5 1138
 
0.2%
33 1001
 
0.2%
2 976
 
0.2%
67 797
 
0.2%
Other values (453) 9391
 
1.8%
ValueCountFrequency (%)
0 1989
0.4%
1 2495
0.5%
2 976
 
0.2%
3 2466
0.5%
4 1593
0.3%
5 1138
0.2%
6 706
 
0.1%
7 1830
0.4%
8 382
 
0.1%
9 616
 
0.1%
ValueCountFrequency (%)
1893 1
 
< 0.1%
1883 1
 
< 0.1%
1834 1
 
< 0.1%
1833 1
 
< 0.1%
1803 1
 
< 0.1%
1800 1
 
< 0.1%
1783 6
< 0.1%
1779 1
 
< 0.1%
1767 9
< 0.1%
1760 1
 
< 0.1%

FREQUENCE
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6525417
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 MiB
2025-07-28T14:57:05.233647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q19
median9
Q39
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.6030191
Coefficient of variation (CV)0.18526568
Kurtosis17.728371
Mean8.6525417
Median Absolute Deviation (MAD)0
Skewness-4.4195104
Sum4425403
Variance2.5696701
MonotonicityNot monotonic
2025-07-28T14:57:05.510898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
9 487820
95.4%
1 18197
 
3.6%
2 3227
 
0.6%
3 907
 
0.2%
4 520
 
0.1%
5 298
 
0.1%
6 129
 
< 0.1%
10 125
 
< 0.1%
7 113
 
< 0.1%
8 59
 
< 0.1%
Other values (6) 62
 
< 0.1%
ValueCountFrequency (%)
1 18197
 
3.6%
2 3227
 
0.6%
3 907
 
0.2%
4 520
 
0.1%
5 298
 
0.1%
6 129
 
< 0.1%
7 113
 
< 0.1%
8 59
 
< 0.1%
9 487820
95.4%
10 125
 
< 0.1%
ValueCountFrequency (%)
16 7
 
< 0.1%
15 6
 
< 0.1%
14 6
 
< 0.1%
13 8
 
< 0.1%
12 19
 
< 0.1%
11 16
 
< 0.1%
10 125
 
< 0.1%
9 487820
95.4%
8 59
 
< 0.1%
7 113
 
< 0.1%

DATA_VOLUME
Real number (ℝ)

Distinct161
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean256.88412
Minimum157
Maximum317
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 MiB
2025-07-28T14:57:05.642509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum157
5-th percentile257
Q1257
median257
Q3257
95-th percentile257
Maximum317
Range160
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.5054862
Coefficient of variation (CV)0.013646177
Kurtosis463.20091
Mean256.88412
Median Absolute Deviation (MAD)0
Skewness-17.434749
Sum1.3138518 × 108
Variance12.288434
MonotonicityNot monotonic
2025-07-28T14:57:05.789413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
257 509358
99.6%
159 25
 
< 0.1%
172 23
 
< 0.1%
168 23
 
< 0.1%
310 23
 
< 0.1%
192 22
 
< 0.1%
187 21
 
< 0.1%
160 21
 
< 0.1%
209 21
 
< 0.1%
190 21
 
< 0.1%
Other values (151) 1899
 
0.4%
ValueCountFrequency (%)
157 11
< 0.1%
158 18
< 0.1%
159 25
< 0.1%
160 21
< 0.1%
161 15
< 0.1%
162 17
< 0.1%
163 20
< 0.1%
164 17
< 0.1%
165 11
< 0.1%
166 19
< 0.1%
ValueCountFrequency (%)
317 9
 
< 0.1%
316 12
< 0.1%
315 8
 
< 0.1%
314 16
< 0.1%
313 6
 
< 0.1%
312 7
 
< 0.1%
311 11
< 0.1%
310 23
< 0.1%
309 10
< 0.1%
308 17
< 0.1%

ON_NET
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 MiB
27.0
511457 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2045828
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row27.0
2nd row27.0
3rd row27.0
4th row27.0
5th row27.0

Common Values

ValueCountFrequency (%)
27.0 511457
100.0%

Length

2025-07-28T14:57:05.948217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T14:57:06.035937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
27.0 511457
100.0%

Most occurring characters

ValueCountFrequency (%)
2 511457
25.0%
7 511457
25.0%
. 511457
25.0%
0 511457
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1534371
75.0%
Other Punctuation 511457
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 511457
33.3%
7 511457
33.3%
0 511457
33.3%
Other Punctuation
ValueCountFrequency (%)
. 511457
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2045828
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 511457
25.0%
7 511457
25.0%
. 511457
25.0%
0 511457
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2045828
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 511457
25.0%
7 511457
25.0%
. 511457
25.0%
0 511457
25.0%

ORANGE
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 MiB
29.0
511457 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2045828
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row29.0
2nd row29.0
3rd row29.0
4th row29.0
5th row29.0

Common Values

ValueCountFrequency (%)
29.0 511457
100.0%

Length

2025-07-28T14:57:06.121001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T14:57:06.186166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
29.0 511457
100.0%

Most occurring characters

ValueCountFrequency (%)
2 511457
25.0%
9 511457
25.0%
. 511457
25.0%
0 511457
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1534371
75.0%
Other Punctuation 511457
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 511457
33.3%
9 511457
33.3%
0 511457
33.3%
Other Punctuation
ValueCountFrequency (%)
. 511457
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2045828
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 511457
25.0%
9 511457
25.0%
. 511457
25.0%
0 511457
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2045828
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 511457
25.0%
9 511457
25.0%
. 511457
25.0%
0 511457
25.0%

TIGO
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 MiB
6.0
511457 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1534371
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6.0
2nd row6.0
3rd row6.0
4th row6.0
5th row6.0

Common Values

ValueCountFrequency (%)
6.0 511457
100.0%

Length

2025-07-28T14:57:06.265848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T14:57:06.338016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
6.0 511457
100.0%

Most occurring characters

ValueCountFrequency (%)
6 511457
33.3%
. 511457
33.3%
0 511457
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1022914
66.7%
Other Punctuation 511457
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 511457
50.0%
0 511457
50.0%
Other Punctuation
ValueCountFrequency (%)
. 511457
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1534371
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 511457
33.3%
. 511457
33.3%
0 511457
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1534371
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 511457
33.3%
. 511457
33.3%
0 511457
33.3%

MRG
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 MiB
0
511457 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters511457
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 511457
100.0%

Length

2025-07-28T14:57:06.433542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T14:57:06.497528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 511457
100.0%

Most occurring characters

ValueCountFrequency (%)
0 511457
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 511457
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 511457
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 511457
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 511457
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 511457
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 511457
100.0%

REGULARITY
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8260851
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 MiB
2025-07-28T14:57:06.561178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile12
Maximum16
Range15
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.5348028
Coefficient of variation (CV)0.92386937
Kurtosis1.7636715
Mean3.8260851
Median Absolute Deviation (MAD)1
Skewness1.5451319
Sum1956878
Variance12.494831
MonotonicityNot monotonic
2025-07-28T14:57:06.686855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 169305
33.1%
2 92381
18.1%
3 59041
 
11.5%
4 41646
 
8.1%
5 30946
 
6.1%
6 23814
 
4.7%
7 18788
 
3.7%
8 15156
 
3.0%
9 12297
 
2.4%
10 10327
 
2.0%
Other values (6) 37756
 
7.4%
ValueCountFrequency (%)
1 169305
33.1%
2 92381
18.1%
3 59041
 
11.5%
4 41646
 
8.1%
5 30946
 
6.1%
6 23814
 
4.7%
7 18788
 
3.7%
8 15156
 
3.0%
9 12297
 
2.4%
10 10327
 
2.0%
ValueCountFrequency (%)
16 4387
 
0.9%
15 4971
 
1.0%
14 5707
 
1.1%
13 6523
 
1.3%
12 7481
 
1.5%
11 8687
1.7%
10 10327
2.0%
9 12297
2.4%
8 15156
3.0%
7 18788
3.7%

TOP_PACK
Real number (ℝ)

Skewed 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.020148
Minimum18
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 MiB
2025-07-28T14:57:06.799309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile18
Q118
median18
Q318
95-th percentile18
Maximum132
Range114
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3907251
Coefficient of variation (CV)0.077176118
Kurtosis5511.7071
Mean18.020148
Median Absolute Deviation (MAD)0
Skewness73.611803
Sum9216531
Variance1.9341163
MonotonicityNot monotonic
2025-07-28T14:57:06.918427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
18 511311
> 99.9%
123 59
 
< 0.1%
30 37
 
< 0.1%
126 18
 
< 0.1%
77 13
 
< 0.1%
31 6
 
< 0.1%
125 4
 
< 0.1%
40 3
 
< 0.1%
44 2
 
< 0.1%
93 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
ValueCountFrequency (%)
18 511311
> 99.9%
30 37
 
< 0.1%
31 6
 
< 0.1%
40 3
 
< 0.1%
44 2
 
< 0.1%
77 13
 
< 0.1%
82 1
 
< 0.1%
93 1
 
< 0.1%
96 1
 
< 0.1%
123 59
 
< 0.1%
ValueCountFrequency (%)
132 1
 
< 0.1%
126 18
 
< 0.1%
125 4
 
< 0.1%
123 59
< 0.1%
96 1
 
< 0.1%
93 1
 
< 0.1%
82 1
 
< 0.1%
77 13
 
< 0.1%
44 2
 
< 0.1%
40 3
 
< 0.1%

FREQ_TOP_PACK
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 MiB
5.0
511457 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1534371
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.0
3rd row5.0
4th row5.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0 511457
100.0%

Length

2025-07-28T14:57:07.045758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T14:57:07.120642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5.0 511457
100.0%

Most occurring characters

ValueCountFrequency (%)
5 511457
33.3%
. 511457
33.3%
0 511457
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1022914
66.7%
Other Punctuation 511457
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 511457
50.0%
0 511457
50.0%
Other Punctuation
ValueCountFrequency (%)
. 511457
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1534371
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 511457
33.3%
. 511457
33.3%
0 511457
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1534371
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 511457
33.3%
. 511457
33.3%
0 511457
33.3%

CHURN
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 MiB
1
285307 
0
226150 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters511457
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 285307
55.8%
0 226150
44.2%

Length

2025-07-28T14:57:07.201118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-28T14:57:07.280354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 285307
55.8%
0 226150
44.2%

Most occurring characters

ValueCountFrequency (%)
1 285307
55.8%
0 226150
44.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 511457
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 285307
55.8%
0 226150
44.2%

Most occurring scripts

ValueCountFrequency (%)
Common 511457
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 285307
55.8%
0 226150
44.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 511457
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 285307
55.8%
0 226150
44.2%

Interactions

2025-07-28T14:57:00.327489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:39.471100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:41.484245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:43.421890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:45.967299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:48.106105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:50.085424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:51.965368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:54.092579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:56.020767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:58.365924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:57:00.492835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:39.649993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:41.656264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:43.592299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:46.270034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:48.267724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:50.248124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:52.151576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:54.262034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:56.196873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:58.540850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:57:00.659500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:39.837465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:41.836827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:43.760920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:46.483736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:48.437284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:50.418050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:52.330520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:54.442424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:56.362519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:58.727875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:57:00.840818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:40.107731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:42.007469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:43.937099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:46.707271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:48.602230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:50.591005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:52.510447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:54.615445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:56.556684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:58.928236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:57:01.062457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:40.282075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:42.192268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:44.119274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:46.915228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:48.767455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:50.768052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:52.730279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:54.795001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:56.754705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:59.108949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:57:01.262333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:40.439530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:42.374358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:44.292904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:47.097137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:48.933885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:50.941759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:52.960989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:54.976357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:56.976904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:59.278184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:57:01.446395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:40.607870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:42.541171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:44.467566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:47.257698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:49.083911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:51.101166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:53.153852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:55.149430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:57.193447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:59.448701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:57:01.627475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:40.793700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:42.716251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:44.647813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:47.419181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:49.409300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:51.281250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:53.343051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:55.322654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:57.409975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:59.611652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:57:01.806166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:40.955855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:42.896906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:44.834859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:47.583547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:49.579957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:51.452938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:53.531861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:55.497208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:57.626621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:59.791861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:57:01.998999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:41.143916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:43.075888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:45.022798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:47.766391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:49.733454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:51.634006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:53.720659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:55.674041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:57.821542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:59.977656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:57:02.158680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:41.305548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:43.249578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:45.762719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:47.945563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:49.920636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:51.806962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:53.917946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:55.845490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:56:58.182975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-28T14:57:00.158220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-28T14:57:07.360203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ARPU_SEGMENTCHURNDATA_VOLUMEFREQUENCEFREQUENCE_RECHMONTANTREGIONREGULARITYREVENUETENURETOP_PACKuser_id
ARPU_SEGMENT1.0000.0440.0040.9770.1790.1770.019-0.0661.000-0.005-0.070-0.002
CHURN0.0441.0000.0310.0460.0590.0580.2580.3350.0440.0450.0110.001
DATA_VOLUME0.0040.0311.0000.0050.0070.006-0.010-0.0140.004-0.004-0.0200.001
FREQUENCE0.9770.0460.0051.0000.1770.1670.019-0.0620.977-0.005-0.068-0.002
FREQUENCE_RECH0.1790.0590.0070.1771.0000.976-0.0130.0020.179-0.004-0.0650.002
MONTANT0.1770.0580.0060.1670.9761.000-0.0090.0030.177-0.003-0.0650.002
REGION0.0190.258-0.0100.019-0.013-0.0091.0000.1250.0190.0170.006-0.000
REGULARITY-0.0660.335-0.014-0.0620.0020.0030.1251.000-0.0660.0330.0190.002
REVENUE1.0000.0440.0040.9770.1790.1770.019-0.0661.000-0.005-0.070-0.002
TENURE-0.0050.045-0.004-0.005-0.004-0.0030.0170.033-0.0051.0000.0020.002
TOP_PACK-0.0700.011-0.020-0.068-0.065-0.0650.0060.019-0.0700.0021.000-0.001
user_id-0.0020.0010.001-0.0020.0020.002-0.0000.002-0.0020.002-0.0011.000

Missing values

2025-07-28T14:57:02.343925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-28T14:57:02.833483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

user_idREGIONTENUREMONTANTFREQUENCE_RECHREVENUEARPU_SEGMENTFREQUENCEDATA_VOLUMEON_NETORANGETIGOMRGREGULARITYTOP_PACKFREQ_TOP_PACKCHURN
11053000.07.03000.01000.09.0257.027.029.06.004185.01
66773000.07.03000.01000.09.0257.027.029.06.002185.00
101007200.01.03000.01000.09.0257.027.029.06.001185.00
1212033000.07.03000.01000.09.0257.027.029.06.002185.01
1717043000.07.03000.01000.09.0257.027.029.06.005185.01
202007200.01.03000.01000.09.0257.027.029.06.0012185.00
2222073000.07.03000.01000.09.0257.027.029.06.004185.01
3535073000.07.03000.01000.09.0257.027.029.06.005185.01
4646073000.07.03000.01000.09.0257.027.029.06.008185.00
5151073000.07.03000.01000.09.0257.027.029.06.003185.01
user_idREGIONTENUREMONTANTFREQUENCE_RECHREVENUEARPU_SEGMENTFREQUENCEDATA_VOLUMEON_NETORANGETIGOMRGREGULARITYTOP_PACKFREQ_TOP_PACKCHURN
21540242154024073000.07.03000.01000.09.0257.027.029.06.002185.00
215402721540271273000.07.03000.01000.09.0257.027.029.06.001185.01
21540282154028073000.07.03000.01000.09.0257.027.029.06.008185.00
21540292154029073000.07.03000.01000.09.0257.027.029.06.002185.01
21540322154032073000.07.03000.01000.09.0257.027.029.06.001185.00
21540332154033073000.07.03000.01000.09.0257.027.029.06.006185.01
21540342154034043000.07.03000.01000.09.0257.027.029.06.004185.01
21540412154041073000.07.03000.01000.09.0257.027.029.06.001185.00
21540432154043073000.07.03000.01000.09.0257.027.029.06.006185.00
21540452154045073000.07.03000.01000.09.0257.027.029.06.001185.01